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Objective

The complex set of relationships among economic agents has profound effects on individuals’ behaviour and economic outcomes. This premise, which finds strong empirical support, is at the basis of the recent development of the economic theory of networks. Research on networks in the last 15 years has set up a common tool to model and study complex relationships within the economic paradigm. This project will advance the state of the art by 1) furthering the development of network theory in economics, and 2) developing new applied models of networks in economics.

The project is organised into three, inter-related sub-projects. In sub-project 1, networks will be used to represent different markets architectures. The objectives are to explain how trading mechanisms and the architecture of the network jointly shape the properties of the market equilibrium, and to provide normative insights on how to organize and regulate markets where trading networks are in place. In sub-project 2, networks will be used to model organizations. The objectives are to improve our understanding of the optimal design of an organization, and to provide a rationale for the current empirical organizational trends from hierarchical to more decentralised organizations. In sub-project 3, networks will be used to model communication. The objectives are to improve our understanding of information aggregation in multi-player environments, and to apply these insights to understand the effect of strategic communication on political games.

Final Report Summary - NETWORKS (Networks, Markets and Organizations)

The complex set of relationships amongst economic agents has profound effects on individuals’ behavior and economic outcomes. This premise, which has strong empirical support, is the basis of the recent development in the economic theory of networks. This project advances the state-of-the-art by 1) furthering the development of network theory in economics, and 2) developing new applied models of networks in economics.

In the first part of the project, networks are used to represent different markets architectures. Our study shows that conclusions on how networks shape market outcomes depend on the interplay between the specific protocol of trading and the information that traders have about the market.This calls for a systematic study of empirically relevant trading protocols in combination with different informational assumptions.

We first develop a dynamic model of bilateral trading in networks. With complete information, the seller of the object extracts the entire surplus and the outcome is efficient, regardless of the structure of the network. In contrast, when traders have asymmetric information, the price of the object alternates over time between phases of high prices and phases of low prices. The object is bought and re-sold by many traders, and traders who act as intermediaries attain a profit. This profit depends on their network location. Inefficiencies may arise in equilibrium, but as traders become perfectly patient, equilibrium becomes efficient.

We then propose a model of posted prices in networks. Posted prices are the norm in transportation and communication networks, and they are a good approximation in environments in which trade occurs at a high frequency, e.g. over-the-counter financial markets. The model maps traditional concepts of market power and competition in to networks, allowing for the study of pricing in complex networks of intermediation such as supply chains, transportation and communication networks, and decentralized trading.

Our theoretical and experimental analysis points to node criticality as an organizing principle for understanding pricing, efficiency and distribution of wealth in networked markets. From a policy perspective, these results suggest that facilitating entry in network segments with critical traders improves efficiency; similarly, entry/mergers that shorten distances between source and destination improve efficiency.

In the second part of the project, networks are used to model communication and diffusion of information. This part of the project has led to two distinctive, but complementary, models. The first model views communication as the outcome of purely strategic decisions. An important insight that we obtain is that the amount of information that can be transmitted between two agents depends not only on the intensity of their conflict of interest, but also on the number of others that the two agents are in communication with. We show how this insight has important implications for the optimal design of communication flows, and allocation of decision power in organizations and political institutions. The second class of model views diffusion as the outcome of an epidemic in a social network: we combine standard epidemiological models with individual economic decisions and a pattern of interaction modelled as a random graph. We show that considering the pattern of interaction is very important to optimally design policies that aim at stimulating or preventing diffusion.